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Construction of coincident indicators for the euro area. 5th EUROSTAT Colloquium on Modern Tools For Business Cycle Analysis, Luxembourg, 29th September - 1st October 2008

Author

Listed:
  • Françoise Charpin

    (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po)

  • Catherine Mathieu

    (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po)

  • Gian Luigi Mazzi

    (Eurostat)

Abstract

The availability of timely and reliable information on main macroeconomic variables is considered both by policy makers and analysts as crucial for an effective process of decision making. Unfortunately official statistics cannot always meet adequately user needs. This is the reason why, using econometric techniques analysts try to anticipate or estimate in real time main macroeconomic movements. In this paper we compare several econometric models for the estimation of the period on period growth rate for the euro area Gross Domestic Product (GDP) and Industrial Production Index (IPI). This comparison is made on the basis of real time results provided by these models over six years (2002-2007). Tests of absence of bias are performed and Diebold-Mariano tests help us to select among the models. The paper also presents a new indicator for euro area employment quarterly growth, which seems to perform rather well in the recent past, although this is still a preliminary assessment as we are only at an early stage of running the indicator.

Suggested Citation

  • Françoise Charpin & Catherine Mathieu & Gian Luigi Mazzi, 2008. "Construction of coincident indicators for the euro area. 5th EUROSTAT Colloquium on Modern Tools For Business Cycle Analysis, Luxembourg, 29th September - 1st October 2008," Post-Print hal-01053253, HAL.
    • Handle: RePEc:hal:journl:hal-01053253
    Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-01053253
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    References listed on IDEAS

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    1. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    2. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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